Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series （1960-2006）

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata (e.g., probable dates of break points) are not always available. To examine the influence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily temperature series for 1960--2006 in three cases with different references: (1) 13M---considering metadata at BJ and 12 nearby stations; (2) 13NOM---considering the same 13 stations without metadata; and (3) 21NOM---considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71^oC, -0.79^oC, and -0.5^oC for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365^oC (10 yr)^-1 for the three cases, respectively, smaller than the estimate of 0.453^oC (10 yr)^-1 in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.     

由最高最低气温求算的平均气温对我国年平均气温序列影响

针对研究全国近百年平均气温长期变化的实际需要,利用603个测站1961—2002年气温观测资料,比较分析了最高最低平均气温距平序列和4次观测记录平均气温距平序列的差异,讨论了最高、最低气温变化趋势。结果表明：两种统计方法得到的平均气温距平序列及增温速率的差异均不明显,在一定条件下两者可以互相替换。此外,最高、最低气温变化普遍存在不对称现象,且可分为4种类型,这种不对称性对平均气温变化速率并没有明确一致的影响。     

Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.     

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature(SAT) series at 32 stations in China back to the 19 th century had previously been developed based on the RHtest method by Cao et al.,but some inhomogeneities remained in the dataset.The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization(MASH) method.The MASH procedure detects 33 monthly temperature records as erroneous outliers and152 meaningful break points in the monthly SAT series since 1924 at 28 stations.The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series.The new data show significant warming trends during 1924–2016 at all the stations,ranging from 0.48 to 3.57?C(100 yr)~(-1),with a regional mean trend of 1.65?C(100 yr)~(-1);whereas,the previous results ranged from a slight cooling at two stations to considerable warming,up to 4.5?C(100 yr)~(-1).It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century.     

Homogenization of the Daily Land Surface Temperature over the Mainland of China from 1960 through 2017

Land surface temperature(LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other non-climatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017. The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40°N due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization(MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68 × 103 effective breakpoints in 1.65 × 106 monthly records(about 20%) are detected. A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China. After the MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/– 0.5°C, and of the remaining breakpoints, only 10% are adjusted over 1.5°C.Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend [0.22°C(10 yr)–1]. The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).     

华东地区最高最低气温时空变化特征

利用华东地区1961-2005年气象站点的气温资料,分析了华东地区最高、最低气温的时间和空间变化特征。结果表明,1961-2005年华东最高和最低气温均有先降后升的变化趋势,其中冬季增温幅度最大,春季次之,夏季最高气温变化趋势不明显,最低气温升温幅度最小。2001-2005年,华东地区平均最高和最低气温最高;最高气温在20世纪80年代最低,最低气温在60年代最低。最高气温和最低气温分别在1989年和1988年发生了增加突变。华东最高和最低气温在空间上基本都为增温。华东最高气温在沿海地区的增温幅度大多大于内陆地区。最低气温在安徽北部、江苏北部和山东半岛的部分地区增温幅度较大,而在山东西部、江西西北部、浙江西部及福建沿海的部分地区增温幅度较小。最高、最低气温除在冬季增温趋势一致,即北部增温幅度大于南部外,在其他三个季节都呈现出不同的空间变化特征。     

山东近百年来的最高、最低温度变化

对济南(1919～2002年)、青岛(1900～2002年)年及四季的平均和平均最低(高)气温序列进行诊断分析，结果表明：近百年来年平均最低气温和平均气温都呈上升趋势，但前者的增温率明显高于后者；最高气温青岛呈上升趋势，而济南却呈下降趋势；济南、青岛出现两次突然增暖，其中30年代的增暖以白天增暖较多，而80年代以来的增暖主要发生于夜间；由于日夜增暖的不对称性，导致第二次增暖期比第一次增暖期气温日较差明显变小。     

1951～2002年中国平均最高、最低气温及日较差变化

利用1951～2002年全国733个台站的月平均最高、最低气温资料,对我国年、季平均最高、最低气温变化趋势的空间分布状况和时间变化特征进行了分析.结果表明:近52年来,我国平均最高气温的变化特征呈现北方增暖明显、南方变化不明显或呈弱降温趋势;年平均最低气温全国各地基本一致,呈明显的变暖趋势;无论是年还是季,平均最低气温的增暖幅度明显大于平均最高气温的增幅;我国年平均日较差多呈下降趋势,并在我国北方地区尤为明显,各季平均日较差亦均呈下降趋势,并以冬季的下降幅度为最大;年平均最高气温和最低气温的变化在年代际变化上基本呈现较为一致的步伐,即52年来主要的变暖均是从20世纪80年代中期开始,均在90年代后期达到了近52年来的历史新高,近年来又略有回落.     

最高最低气温出现时间分析

对新乡气象站2012-2013年每目的最高最低气温出现时间进行分析发现,在正常出现时间之外,有许多异常情况。剔除异常时间点,对正常时间进行统计得到：日最高气温冬半年平均出现在14～15时,夏半年在15～16时;最低气温冬季在07时左右,夏季在05：30前后。两年中有22％的温度极值在异常时间点出现。异常极值的出现主要由冷锋、强冷空气、云、降水、风、雾、霾等因素引起。当气温日较差比较小时,气温极值出现的时间和地点存在随机性。     

利用日最高气温和日最低气压预报5月大风

对1981－1995年的大风资料进行统计分析，得出用日最高气温和日最低气压预报于田县5月大风天气的指标。